Advances in computing systems enable content to be produced easily and also to be distributed to end users through a variety of channels, e.g., different web sites, different applications on a variety of devices, and so on. As a result, the amount of content available to users is not only is staggeringly large, but also continues to grow. Given the amount of available content, users are unlikely to know about the entirety of this content. To this extent, recommendation systems provide digital content recommendations to users for items these systems predict the users will like. These recommendation systems provide digital content recommendations for a variety of items including, but not limited to, videos, music, audiobooks, e-books or periodicals, news articles, products or services, and so forth.
Broadly speaking, there are two types of conventional recommendation systems. The first type of recommendation systems are item-based. Item-based recommendation systems generally provide a same recommendation to different client device users that consume a same digital content item. For instance, if two different users view a same video, item-based recommendation systems recommend a same set of videos to watch next. This approach does not personalize recommendations, however. Typically, personalized recommendations lead to higher user engagement and retention than non-personalized recommendations. The second type of conventional systems, user-based recommendation systems, can provide personalized recommendations. In general, user-based recommendation systems learn a model for a given user's behavior, e.g., based on the given user's interaction history with digital content items, purchase history, and so forth. In particular, these user-based recommendation systems learn this model in the form of latent factors. While such latent factors capture some aspect of user behavior, the captured aspect is often computer, but not human, interpretable. Due to capturing aspects that are not human-interpretable, the recommendations of user-based systems cannot be provided with corresponding human-interpretable justifications that explain why the recommendations are provided. Though these recommendations recommend items that client device users may find more interesting than other items, if the recommendations are heeded, client device users may not be comfortable interacting with such recommendations because they lack corresponding justifications. Accordingly, client device users may not interact with recommendations provided using these conventional techniques.